DanceText: A Training-Free Layered Framework for Controllable Multilingual Text Transformation in Images

• URL: http://arxiv.org/abs/2504.14108v2
• Date: Fri, 26 Sep 2025 02:03:49 GMT
• Title: DanceText: A Training-Free Layered Framework for Controllable Multilingual Text Transformation in Images
• Authors: Zhenyu Yu, Mohd Yamani Idna Idris, Hua Wang, Pei Wang, Rizwan Qureshi, Shaina Raza, Aman Chadha, Yong Xiang, Zhixiang Chen,
• Abstract summary: DanceText is a training-free framework for multilingual text editing in images.<n>It supports complex geometric transformations and achieves seamless foreground-background integration.
• Score: 28.48453375674059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present DanceText, a training-free framework for multilingual text editing in images, designed to support complex geometric transformations and achieve seamless foreground-background integration. While diffusion-based generative models have shown promise in text-guided image synthesis, they often lack controllability and fail to preserve layout consistency under non-trivial manipulations such as rotation, translation, scaling, and warping. To address these limitations, DanceText introduces a layered editing strategy that separates text from the background, allowing geometric transformations to be performed in a modular and controllable manner. A depth-aware module is further proposed to align appearance and perspective between the transformed text and the reconstructed background, enhancing photorealism and spatial consistency. Importantly, DanceText adopts a fully training-free design by integrating pretrained modules, allowing flexible deployment without task-specific fine-tuning. Extensive experiments on the AnyWord-3M benchmark demonstrate that our method achieves superior performance in visual quality, especially under large-scale and complex transformation scenarios. Code is avaible at https://github.com/YuZhenyuLindy/DanceText.git.

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